code: def evaluate(self, batch, stage=None):
      x, y = batch
      logits = self(x)
      loss = F.nll_loss(logits, y)
      preds = torch.argmax(logits, dim=1)
      acc = accuracy(preds, y, "multiclass", num_classes=10)
  
      if stage:
          self.log(f"default", acc, prog_bar=False)
          self.log(f"{stage}_loss", loss, prog_bar=True)
          self.log(f"{stage}_acc", acc, prog_bar=True)
  
imports: from __future__ import annotations
  import pytorch_lightning
  import torch
  import torch.nn.functional
  from torch.optim.lr_scheduler import ExponentialLR
  from torch.utils.data import random_split, DataLoader
  from torchmetrics.functional import accuracy
  from torchvision.datasets import MNIST
  from torchvision import transforms
  import nni
  from .simple_torch_model import SimpleTorchModel
label_desc: Use image node_id 4 for calling the SimpleLightningModel obj's underlying member method code with example usage: obj.evaluate(batch, stage) without return types